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Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

This course provides a rigorous, ground-up foundation in Topological Data Analysis (TDA) tailored for engineering graduate students. It bridges deterministic topology ? algebraic topology, to be precise, ? with spatial statistics, focusing on uncertainty quantification, Gaussian Random Fields (GRFs), and robust feature extraction using persistent homology. The course emphasizes theoretical guarantees and probabilistic modeling over black-box machine learning integration. Good knowledge of analysis, linear algebra (MATH257 should suffice) and probability theory (at least ECE313) will be expected.